Tag: HAQM Athena

Citizens are increasingly expecting government to provide modern digital experiences for conducting online transactions. Market research tells us 63 percent of consumers see personalization as the standard level of service. This post offers various architectural patterns for improving customer experience for the public sector for a wide range of use cases. The aim of the post is to help public sector organizations create customer experience solutions on the HAQM Web Services (AWS) Cloud using AWS artificial intelligence (AI) services and AWS purpose-built data analytics services.

In this post, we explore the complexities of data privacy and controls on HAQM Web Services (AWS), examine how creating a landing zone within which to contain such data is important, and highlight the differences between creating a landing zone from scratch compared with using the AWS Landing Zone Accelerator (LZA) for Healthcare. To aid explanation, we use a simple healthcare workload as an example. We also explain how LZA for Healthcare codifies HIPAA controls and AWS Security Best Practices to accelerate the creation of an environment to run protective health information workloads in AWS.

HAQM SageMaker is a fully managed machine learning (ML) service that provides various capabilities, including Jupyter Notebook instances. While RStudio, a popular integrated development environment (IDE) for R, is available as a managed service in HAQM Web Services (AWS) commercial Regions, it’s currently not offered in AWS GovCloud (US) Regions. Read this post, however, to learn how you can use SageMaker notebook instances with the R kernel to perform data analytics tasks in AWS GovCloud (US) Regions.

This post discusses how HAQM Web Services (AWS) can help you successfully set up an HAQM DataZone domain, aggregate data from multiple sources into a single centralized environment, and perform analytics on that data. Additionally, this post provides a sample architecture as well as a walkthrough on how to set up that architecture. Ultimately, this post serves as a valuable resource if you’re seeking to optimize your data management processes and derive actionable insights to drive business growth.

Introduction Government authorities such as tax, unemployment insurance, and other finance agencies across the US and globally are seeking ways to innovate. They are trying to unlock insights from their data, deliver better customer experiences, and improve operations using cutting-edge technologies such as generative artificial intelligence (AI), machine learning (ML), and other data analytics tools. […]

As the amount of health data increases, different healthcare, life sciences, population health, and public health organizations are working to modernize their data infrastructure, unify their data, and innovate faster with technologies like artificial intelligence and machine learning (AI/ML). In this blog post, we dive deep on architecture guidance that enables healthcare providers to improve patient care.

What if paper forms could be processed at the same speed as digital forms? What if their contents could be automatically entered in the same database as the digital forms? Medicaid agencies could analyze data in near real time and drive actionable insights on a single dashboard. By using artificial intelligence (AI) and machine learning (ML) services from AWS, Medicaid agencies can create this streamlined solution. In this walkthrough, learn how to extract, analyze, and interpret relevant information from paper-based Medicaid claims forms.

In 2020, Meta introduced the Daylight Map Distribution, which combines OpenStreetMap (OSM) data with quality and consistency checks from Daylight mapping partners to create a no-cost, stable, and simple-to-use global map. This blog post provides a brief overview of OSM and Daylight followed by a step-by-step tutorial using five real-world examples. We combine the powerful query capabilities of HAQM Athena from with the feature-rich Daylight OSM data to demonstrate a typical OSM data analysis workflow.

In this guest post, Ajay K. Gupta, co-founder and chief executive officer (CEO) of HSR.health, explains how healthcare technology (HealthTech) nonprofit HSR.health uses geospatial artificial intelligence and AWS to develop solutions that support improvements in healthcare and health equity around the world.

At AWS, many public sector customers operate fleets of vehicles (e.g. emergency response, public transportation) that generate location data, which is ultimately stored in a data lake. These customers frequently ask how they can quickly visualize this data and extract insights that can help them optimize how they operate their vehicle fleets. In this post, learn how to use HAQM Athena and HAQM Location Service to perform ad hoc reverse geocoding on a notional dataset of vehicle location history, and visualize the results on an HAQM QuickSight map.